Effect of Visual Range on Driving Speed on Low-grade Highway

AbstractVisual range from road alignments plays an important role in traffic safety since it is a key factor for drivers to adopt an appropriate speed. It is especially true to low-grade highways where speeds tend to reach a high value when sections have a relatively good linearity. High speeds on low-grade highway are considered a main contributor to traffic accidents. This paper studied the relationship of maximum visual range and driving speed on low-grade highways to put forward a suggested visual range for safe driving. Four typically mountainous highway models with the same driving environment but different visual ranges were constructed to analyze the effect of visual range on driving speed. A driving simulator with eight degrees of freedom was used to carry out the experiments. Data of driving speed, acceleration and deceleration were collected to study the speed and acceleration trends. The conclusion drawn is that the appropriate value of maximum visual range on low-grade highways is recommended to be 80m to ensure safety driving

Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data

This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were compared, and an end-to-end multivariate time series clustering system called Time2Feat, combined with K-Means, was chosen as the most effective method to cluster patients in the ICU. In clustering analysis, data of 8,080 patients admitted between 2008 and 2016 was used for model development and 2,038 patients admitted between 2017 and 2019 for model validation. By analyzing the differences in clinical mortality prognosis among different categories, varying risks of ICU mortality and hospital mortality were found between different subgroups. Furthermore, the study visualized the trajectory of vital signs changes. The findings of this study provide valuable insights into the potential use of multivariate time-series clustering systems in patient management and monitoring in the ICU setting.Comment: Proceedings of Beijing Health Data Science Summit (HDSS) 202

Meta-optimized Contrastive Learning for Sequential Recommendation

Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most existing CL methods only perform either hand-crafted data or model augmentation for generating contrastive pairs to find a proper augmentation operation for different datasets, which makes the model hard to generalize. Additionally, since insufficient input data may lead the encoder to learn collapsed embeddings, these CL methods expect a relatively large number of training data (e.g., large batch size or memory bank) to contrast. However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation. Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the model augmenters, which helps to improve the quality of contrastive pairs without enlarging the amount of input data. Finally, a contrastive regularization term is considered to encourage the augmentation model to generate more informative augmented views and avoid too similar contrastive pairs within the meta updating. The experimental results on commonly used datasets validate the effectiveness of MCLRec.Comment: 11 Pages,8 figure

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available

Quality of life in rectal cancer patients with permanent colostomy in Xi’an

Purposes: The aim of this study was to observe the quality of life (QOL) in rectal cancer patients with permanent colostomy in different periods after operation. Methods: A 1-,3-,6-month prospective study of QOL in 51 rectal cancer patients with permanent colostomy and 50 ones without permanent colostomy was assessed by using European Organization for Research and Treatment of Cancer (EORTC) QOL-30 and CR38 questionnaires. Results: The variation of QOL in different periods was “v” type. In the 1st postoperative month, these patients had the lowest quality of life scores, accompanied significantly varied functions and severe symptoms. Almost of all indexes of these patients had improved consistently in postoperative periods. The scores of global QOL even better than pre-operative level at 6th months post-operation, but the social function, body image, chemotherapy side effects and financial difficulties had not restored to the baseline level. Patients without permanent colostomy had a better score in most of categories of QOL-30 and CR38. Conclusions: The 1st postoperative month was crucial for patients’ recovery, in which we should pay great attention to these problems which relate to the recovery of rectal cancer patients with permanent colostomy.Keywords: Quality of life, Rectal cancer, Permanent colostomy, EORTC QOL-30 and CR38 questionnairesAfrican Health sciences Vol 14 No. 1 March 201

OPTIMIZATION AND TEST OF THE OPERATING PARAMETERS OF A CAMELLIA OLEIFERA FRUIT PICKING DEVICE UNDER THE SYNERGISTIC ACTION OF A VIBRATION COMB

ABSTRACT There are significant differences in the quantities of Camellia oleifera fruits growing within and outside the canopy distribution, meaning that a single mode of picking has drawbacks. To improve the harvesting efficiency for the inner and outer layers, this article proposes a synergistic mode of harvesting based on a vibration comb brush, and presents a design for a harvesting device based on this principle. The overall structure and working principle of the proposed device are explained, and the operational processes of the vibration and comb parts of the device are analyzed. ADAMS software is used to construct a rigid-flexible coupling model of the device and the fruiting branch, and simulation results are presented to show that the fruit drop and flower loss rates for Camellia oleifera are related to the vibration frequency, the amplitude, and the spacing between the teeth and comb plates. Finally, a three factor and three level field orthogonal experiment was conducted, and the results showed that under the conditions of vibration frequency 5.85Hz, amplitude 60.43mm, and comb spacing 45mm, the flower loss rate was the lowest and it had good picking performance. Under these conditions, the fruit drop rate is 87.32% and the flower loss rate is 8.06%, values that meet the requirements for mechanized picking of Camellia oleifera fruits

Rewiring carbon flow in Synechocystis PCC 6803 for a high rate of CO2-to-ethanol under an atmospheric environment

Cyanobacteria are an excellent microbial photosynthetic platform for sustainable carbon dioxide fixation. One bottleneck to limit its application is that the natural carbon flow pathway almost transfers CO2 to glycogen/biomass other than designed biofuels such as ethanol. Here, we used engineered Synechocystis sp. PCC 6803 to explore CO2-to-ethanol potential under atmospheric environment. First, we investigated the effects of two heterologous genes (pyruvate decarboxylase and alcohol dehydrogenase) on ethanol biosynthesis and optimized their promoter. Furthermore, the main carbon flow of the ethanol pathway was strengthened by blocking glycogen storage and pyruvate-to-phosphoenolpyruvate backflow. To recycle carbon atoms that escaped from the tricarboxylic acid cycle, malate was artificially guided back into pyruvate, which also created NADPH balance and promoted acetaldehyde conversion into ethanol. Impressively, we achieved high-rate ethanol production (248 mg/L/day at early 4 days) by fixing atmospheric CO2. Thus, this study exhibits the proof-of-concept that rewiring carbon flow strategies could provide an efficient cyanobacterial platform for sustainable biofuel production from atmospheric CO2

Efficacy and safety of the compound Chinese medicine SaiLuoTong in vascular dementia: A randomized clinical trial

Introduction: No licensed medications are available to treat vascular dementia (VaD). Methods: Patients were randomly assigned to experimental groups (SaiLuoTong [SLT] 360 or 240 mg for groups A and B for 52 weeks, respectively) or placebo group (SLT 360 mg and 240 mg for group C only from weeks 27 to 52, respectively). Results: Three hundred twenty-five patients were included in final analysis. At week 26, the difference in VaD Assessment Scale-cognitive subscale scores was 2.67 (95% confidence interval, 1.54 to 3.81) for groups A versus C, and 2.48 (1.34 to 3.62) for groups B versus C (both Discussion: This study suggests that SLT is effective for treatment of VaD, and this compound Chinese medicine may represent a better choice to treat VaD